# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
# Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from clr import AddReference
AddReference("System.Core")
AddReference("System.Collections")
AddReference("QuantConnect.Common")
AddReference("QuantConnect.Algorithm")

from System import *
from System.Collections.Generic import List
from QuantConnect import *
from QuantConnect.Algorithm import QCAlgorithm
from QuantConnect.Data.UniverseSelection import *

### <summary>
### Demonstration of using coarse and fine universe selection together to filter down a smaller universe of stocks.
### </summary>
### <meta name="tag" content="using data" />
### <meta name="tag" content="universes" />
### <meta name="tag" content="coarse universes" />
### <meta name="tag" content="fine universes" />
class CoarseFineFundamentalComboAlgorithm(QCAlgorithm):

    def Initialize(self):
        '''Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.'''

        self.SetStartDate(2014,01,06)  #Set Start Date
        self.SetEndDate(2014,01,07)    #Set End Date
        self.SetCash(50000)            #Set Strategy Cash

        # what resolution should the data *added* to the universe be?
        self.UniverseSettings.Resolution = Resolution.Daily

        # this add universe method accepts two parameters:
        # - coarse selection function: accepts an IEnumerable<CoarseFundamental> and returns an IEnumerable<Symbol>
        # - fine selection function: accepts an IEnumerable<FineFundamental> and returns an IEnumerable<Symbol>
        self.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionFunction)
        
        # Set dictionary of indicators
        self.indicator = {}

        self.__numberOfSymbols = 100
        self.__numberOfSymbolsFine = 5
        self._changes = SecurityChanges.None


    # sort the data by daily dollar volume and take the top 'NumberOfSymbols'
    def CoarseSelectionFunction(self, coarse):
        # sort descending by daily dollar volume
        sortedByDollarVolume = sorted(coarse, key=lambda x: x.DollarVolume, reverse=True)

        # return the symbol objects of the top entries from our sorted collection
        return [ x.Symbol for x in sortedByDollarVolume[:self.__numberOfSymbols] ]

    # sort the data by P/E ratio and take the top 'NumberOfSymbolsFine'
    def FineSelectionFunction(self, fine):
        
        # sort descending by P/E ratio
        sortedByPeRatio = sorted(fine, key=lambda x: x.OperationRatios.OperationMargin.Value, reverse=False)
        
        # Here we want to get our inititialized indicator
        # We are going to use a dictionary to refer the object that will keep the moving averages
        for cf in fine:
            if cf.Symbol not in self.indicator:
                self.indicator[cf.Symbol] = SymbolData(cf.Symbol)

            # Updates the SymbolData object with current EOD price
            avg = self.indicator[cf.Symbol]
            avg.update(cf.EndTime, cf.Price)

        # take the top entries from our sorted collection
        return [ x.Symbol for x in sortedByPeRatio[:self.__numberOfSymbolsFine] ]

    def OnData(self, data):
        
        # liquidate removed securities
        for security in self._changes.RemovedSecurities:
            if security.Invested:
                self.Liquidate(security.Symbol)
        
        # Set dictionary of indicators
        #self.indicator = {}
        
        self.Log("SECS : ".format(self._changes.AddedSecurities))
        # Create indicator & check Price
        for security in self._changes.AddedSecurities:
            self.indicator[security.Symbol] = self.ATR(security.Symbol, 5, Resolution.Daily)
            
            #self.Log("SECURITY : ".format(self.Securities[security.Symbol]))
            self.Log("SECURITY : ".format(security.Symbol))
            #self.Log("ATR : ".format(self.indicator[security.Symbol].AverageTrueRange.Current.Value))
            self.Log("PRICE : ".format(self.Securities[security.Symbol].Price))
            


    #def OnSecuritiesChanged(self, changes):
        #self._changes = changes
        #self._changes = SecurityChanges.None;


    # this event fires whenever we have changes to our universe
    def OnSecuritiesChanged(self, changes):
        self._changes = changes
        
class SymbolData(object):
    def __init__(self, symbol):
        self.symbol = symbol
        self.indicator = ExponentialMovingAverage(100)
        #self.indicator = AverageTrueRange(5)
        #self.indicator = BollingerBands(5)
        self.scale = 0

    def update(self, time, value):
        if self.indicator.Update(time, value):
            indicator = self.indicator.Current.Value